Long noncoding RNA small nucleolar RNA host genes as prognostic molecular biomarkers in hepatocellular carcinoma: A meta‐analysis

Abstract Background Recently, increasing data have suggested that the lncRNA small nucleolar RNA host genes (SNHGs) were aberrantly expressed in hepatocellular carcinoma (HCC), but the association between the prognosis of HCC and their expression remained unclear. The purpose of this meta‐analysis was to determine the prognostic significance of lncRNA SNHGs in HCC. Methods We systematically searched Embase, Web of Science, PubMed, and Cochrane Library for eligible articles published up to February 2024. The prognostic significance of SNHGs in HCC was evaluated by hazard ratios (HRs) and 95% confidence intervals (CIs). Odds ratios (ORs) were used to assess the clinicopathological features of SNHGs. Results This analysis comprised a total of 25 studies covering 2314 patients with HCC. The findings demonstrated that over‐expressed SNHGs were associated with larger tumor size, multiple tumor numbers, poor histologic grade, earlier lymphatic metastasis, vein invasion, advanced tumor stage, portal vein tumor thrombosis (PVTT), and higher alpha‐fetoprotein (AFP) level, but not with hepatitis B virus (HBV) infection, and cirrhosis. In terms of prognosis, patients with higher SNHG expression were more likely to have shorter overall survival (OS), relapse‐free survival (RFS), and disease‐free survival (DFS). Conclusions In conclusion, upregulation of SNHGs expression correlates with shorter OS, RFS, DFS, tumor size and numbers, histologic grade, lymphatic metastasis, vein invasion, tumor stage, PVTT, and AFP level, suggesting that SNHGs may serve as prognostic biomarkers in HCC.


| INTRODUCTION
According to 2020 Cancer Data, hepatocellular carcinoma (HCC) is the sixth most prevalent cancer and the third leading cause of cancer death worldwide, resulting in a significant disease burden. 1 Metabolic risk factors for HCC are becoming more prevalent despite the hepatitis virus remaining the main cause. 2 At the moment, nondrug treatments include ablation, transarterial chemoembolization (TACE), liver transplantation, and hepatic resection.In the meanwhile, advanced HCC is primarily treated systemically with medications including monoclonal antibodies like nivolumab and small molecule targeted medications like sorafenib and lenvatinib. 3Most patients are diagnosed in the middle or late stages, with a 5-year survival rate below 18%. 4 However, current therapy advances still fail to provide satisfactory outcomes.Therefore, it is critical and vital to develop effective biomarkers early on that can be applied as targeted therapy for HCC.
Long noncoding RNAs (LncRNAs) are noncoding RNAs with a length greater than 200 nucleotides.In the absence of functional open reading frames, they hardly encode any protein. 5Increasing studies have clarified that lncRNAs, initially considered as genomic junk, can act as oncogenes or antioncogenes in cancer, regulating tumor growth, metastasis, metabolism, and progression, and may offer a new method for cancer diagnosis and treatment. 6Multiple lncRNAs about HCC have been found to exhibit abnormal expression and take a role in malignant phenotypes via binding to DNA, RNA, or proteins or by encoding tiny peptides, which could influence the progression of HCC.They were expected to be potential biomarkers for diagnosis and prognosis of HCC. 7 Long noncoding small nucleolar RNA host genes (lnc-SNHGs) are host genes for snoRNAs (small nucleolar RNAs), which are overexpressed in human cancers.When acting as a lncRNA called SNHG, snoRNAs retain full-length transcripts, including exons. 8Five different molecular mechanisms explain how SNHGs work: three are connected to cytoplasmic localization, reducing miRNA bioavailability through molecular sponge activity, limiting translation, and preventing ubiquitination; the other two are related to nuclear localization, interacting with transcription factors, or repressors and altering DNA methylation.SNHGs are crucial in carcinogenesis and cancer progression through influencing DNA, RNA, and protein. 9Additionally, SNHGs take part in the occurrence, growth, and pathophysiology of gastrointestinal cancers by regulating downstream targets.They also correlate with clinicopathological traits such as shorter overall survival (OS), tumor size, lymph node metastasis, and TNM stage. 10In recent years, studies have shown that multiple SNHGs was significantly upregulated in HCC and was strongly associated with clinicopathological features. 11ccording to an examination of experimental data, HCC patients expressing high levels of SNHGs got a worse prognosis.Various studies have confirmed their predictive value, although it is unclear how each of them affects the prognosis of HCC. 12 In 2017, Li et al. 13 published a meta-analysis on SNHGs and HCC prognosis, but there were only five SNHGs included.In recent years, studies involving more SNHGs about HCC have emerged.So for further knowledge, we carried out a meta-analysis on the prognostic value and clinicopathological features of the SNHG family in HCC.

| Literature searching strategies
We prospectively registered the meta-analysis on PROSPERO (CRD42022370591).To filter all pertinent studies up to February 2024, Embase, Web of Science, PubMed, and Cochrane Library were searched online.The following keywords were used in the search: (SNHG1 OR SNHG2 OR SNHG3 OR SNHG4 OR SNHG5 OR SNHG6 OR SNHG7 OR SNHG8 OR SNHG9 OR SNHG10 OR SNHG11 OR SNHG12 OR SNHG13 OR SNHG14 OR SNHG15 OR SNHG16 OR SNHG17 OR SNHG18 OR SNHG19 OR SNHG20 OR SNHG21 OR SNHG22) AND ((HCC) OR (Liver Cancer) OR (Liver Cell Carcinoma) OR (Hepatoma)) AND (prognosis OR prognostic OR outcome).The specific searching strategies in each database were provided in the Table S1.To make sure that all appropriate studies were included, we also manually searched pertinent original article references.Two researchers independently decided which publications should be included and excluded.Disagreements were resolved by consensus after discussion with the third author.

| Inclusion criteria
(A) Patients were definitely diagnosed as HCC, and their SNHG expression was detected by specific methods, (B) "High SNHG" and "low SNHG" groups were created.(C) The correlation between SNHG and prognosis along with clinical features were reported, (D) there was enough data in the studies to generate hazard ratios (HRs) and 95% confidence intervals (CIs), (E) studies were published in English.

| Exclusion criteria
(A) Replicated studies, (B) review, meta, raw letter, cellular, and animal studies, (C) irrelevant topics, (D) inadequate information for calculation of HRs and 95% CIs.

| Data extraction and quality assessment
Data were separately extracted by two researchers using inclusion and exclusion criteria, and any differences were solved by discussion.We extracted the following information: the first author, study country, publication year, sample size, sample type, SNHG type, method of SNHG detection, the cutoff value of SNHG expression, endpoints, extract method of HR, HRs and 95% CIs of OS/relapse-free survival (RFS) /disease-free survival (DFS) reported or estimated from survival curves, and clinical characteristics including age, sex, lymphatic metastasis, vein invasion, tumor size, tumor stage, histologic grade.If only Kaplan-Meier curves were given, we could calculate HR and 95% CI with the Engage Digitizer v11.1 software based on the strategy suggested by Tierney. 14he Newcastle-Ottawa Quality Assessment Scale (NOS) was employed to examine the quality of the included studies.The NOS scale 15 considers inclusion, outcome, and comparability, with scores from 0 to 9. Studies with a score of 6 or higher were included in the meta-analysis.

| Statistical analysis
The STATA 15.0 software was used to conduct this metaanalysis of all eligible studies.The relationship between SNHG expression and the prognosis of HCC patients was evaluated by HRs and 95% CIs.Additionally, odds ratios (ORs) and 95% CIs were used to analyze the association between clinicopathological traits and SNHG.We analyzed heterogeneity between studies with the Chi-square Q test and I 2 statistic.The random-effects model was applied if there was significant heterogeneity (χ 2 test p <0.1 or I 2 >50%); otherwise, a fixed-effects model was utilized.A sensitivity analysis was conducted to check whether the findings were stable.Begg's and Egger's tests were used to assess publication bias.Significant differences were defined as p-values <0.05.

| Search process and features of included literature
The detailed search and inclusion process is shown in Figure 1.There were 533 pieces of literature found overall in the initial search.We chose 55 studies strictly based on the criteria for inclusion and exclusion.Then, we further excluded 30 studies upon reviewing all full text of the remaining 55 articles.Ultimately, this meta-analysis included 25 publications (2314 patients).  The mple size was between 40 and 160, with publication years ranging from 2016 to 2022.Except for one, 38 all of the listed studies were carried out in China.These eligible studies included 2314 patients with 13 types of SNHGs, involving SNHG1, 19,35 SNHG3, 20,29 SNHG6, 16,21 SNHG7, 27,28,31,32 SNHG9, 38 SNHG10, 25 SNHG11, 30 SNHG12, 22 SNHG15, 18,34 SNHG16, 24,26,33,40 SNHG17, 37 SNHG18, 39 SNHG20, 17,23 and SNHG22. 36Based on SNHG expression, the enrolled patients were divided into the "high SNHG" or "low SNHG" group.Apart from one study that identified SNHG expression in venous blood, 38 all of the research detected SNHG expression in tissue by qRT-PCR or ISH.The correlation between SNHG and OS was explored in every study.Of them, eight studies also reported the correlation between SNHG and DFS/RFS.Thirteen studies gave HR values directly, while Kaplan-Meier curves were available for calculating HR values in the remaining studies.All included studies had NOS scores ≥6.The characteristics of the included literature are detailed in Table 1.

| SNHG expression and OS
To analyze the relationship of SNHG expression with OS, we selected 25 relevant studies with 2314 participants.The combined results showed a significant association between higher SNHG expression and shorter OS (HR: 2.22, 95% CI: 1.77-2.78,p: 0.000, Figure S1).The HR and 95% CI of OS were analyzed using a random-effects model due to the heterogeneity that existed among the studies (I 2 : 70.5%, p: 0.000).Sensitivity analysis shown in Figure S2 revealed that "Lan T 2019" greatly influenced the stability of the results, which may be a possible source of heterogeneity.Then, after removing "Lan T 2019", the new results showed no heterogeneity (I 2 : 0.00%, p: 0.896), indicating that Lan T 2019 was the source of heterogeneity (Figure 2A).As a result, we finally included 24 qualified studies with 2250 patients.A fixed-effects model determined that the total HR and 95% CI were significant statistically and showed that patients with upregulated SNHGs had a higher risk of having a short OS (HR: 2.33, 95% CI: 2.02-2.69;p: 0.000).The results appeared to be trustable, as demonstrated in the sensitivity analysis of Figure S3.Furthermore, in order to carry out a subgroup analysis of OS, all included patients were grouped according to SNHG type, extract method of HR, follow-up time, and sample size (Table 2).Increased expression of SNHG1, 3, 6, 7, 15, 16, and 20 was substantially connected with poor OS in the SNHG type subgroup, as shown in Figure S4, and other SNHGs were as well.We came to the conclusion that increased expression of SNHGs could result in worse OS both in the group of reported and survival curve when the studies were categorized using the HR extraction approach (reported: HR: 2.44, 95% CI: 2.05-2.90,p: 0.000; survival curve: HR: 2.12, 95% CI: 1.64-2.73,p: 0.000).In addition, we discovered that HCC patients with a worse prognosis had higher levels of SNHG expression regarding follow-up time and sample size.The mentioned data supported that SNHGs in HCC patients could serve as prognostic markers for the intervention of OS.

| SNHG and lymphatic metastasis and vein invasion
There were 5 studies involving 387 participants examined the association of SNHG expression and lymphatic metastasis.The total OR and 95% CI were calculated using a random-effects model due to the heterogeneity among studies (I 2 : 75.5%, p: 0.003; Figure 3E).According to the findings, early lymph node metastasis was linked to upregulated SNHG expression in HCC patients (OR: 6.28, 95% CI: 2.31-17.10;p: 0.000).Meanwhile, patients with high SNHG expression had a higher risk of vein invasion than those with low expression, according to the pooled results from eight high-quality articles (Figure 3F, OR: 2.87, 95% CI:

| Publication bias
The funnel plot, Begg's test, and Egger's test were used to examine potential publication bias.The meta-analysis  Due to the elevated activation of carcinogenic lncRNAs in various cancers in adjacent tissues, concern over lncR-NAs as diagnostic or prognostic markers is growing. 42imilarly, The SNHGs may also be promising indicators for the detection and prognosis of cancer according to many research.It has been extensively studied how SNHGs contribute to the growth of HCC.SNHGs can influence the biologically malignant behavior of tumors through acting as endogenous RNAs (ceRNAs) to adsorb numerous miRNAs, directly binding to and upregulating mRNA, interacting with transcription factors to activate transcription, and activating the signaling pathways such as Wnt/β-catenin that are primarily part in tumor growth.They significantly affected the outcome of HCC patients by promoting growth and metastasis or interfering with apoptosis and autophagy in HCC. 12 Zhang et al 19 stated SNHG1 drastically improved the proliferation and migration of HCC by suppressing the activity of p53 and its target genes, which also inhibited the apoptosis process.According to the study by Xie et al, 32 SNHG7, which was highly expressed in HCC, might compete with miR-9-5p as a ceRNA.As a result, it could upregulate the activity of the CNNM1, which encouraged the growth of HCC in turn.The majority of SNHGs functioned as ceRNAs to promote malignancy in HCC.In addition, SNHGs also played important roles in HCC via additional mechanisms.For example, in HCC, SNHG3, 29 SNHG7, 28 and SNHG20 23 promoted epithelialto-mesenchymal transition (EMT), and SNHG16 40 significantly activated the ECM-receptor interaction pathway, and so on.To sum up, mechanisms of SNHGs varied in HCC.Therefore, we carried out a further analysis to determine if SNHGs were valuable in the prognosis of HCC.
In our analysis to summarize the relationship on prognosis and clinicopathological parameters with SNHG in HCC, a total of 25 eligible papers satisfying inclusion criteria were taken into account.Patients with high SNHG expression had a higher chance of undergoing shorter OS, DFS, and RFS compared to those with low expression.Additionally, the association between SNHG and OS under various situations was further investigated with subgroup analysis.All pooled results showed that SNHG overexpression was related to worse OS of HCC in subgroups of SNHG type, extract method, follow-up time, and sample size.
The correlation on SNHG expression and various clinicopathological features in HCC was also explored at the same time.Patients who had high levels of SNHGs expression were inclined to have larger tumor size, multiple tumors, worse histologic grades, more advanced tumor stage, positive lymphatic metastasis, vein invasion, PVTT, and AFP values >400ug/L.In contrast, SNHG expression had no bearing on a range of other clinical characteristics, including age, gender, HBV infection, and cirrhosis.Taking into account all of these findings, lncRNA SNHGs played a part in the emergence of HCC and had the potential to grow into a biomarker for the prognosis.
Our findings were consistent with a meta-analysis 13 on SNHG and HCC prognosis in 2017, which covered only 5 papers at that time.In recent years, more studies on this subject have been published.Then, we reanalyzed the association between SNHG and clinical features along with the prognosis of HCC, which appeared more convincing and detailed.
However, we should acknowledge some limitations of the study.Firstly, the results couldn't be generalized to other nations because all but one of the research included in it were from China, which could have caused publication bias.Secondly, there was a chance of errors because the HRs and 95% CIs were partially extracted indirectly from the survival curves.Thirdly, the cutoff values of SNHG varied between research, which could have an impact on the relevant outcomes.Lastly, only a portion of SNHGs was involved in our analysis due to insufficient research and sample size.

F I G U R E 1
PRISMA flowchart of the selection process.T A B L E 1 Details of included studies.

F I G U R E 2
Forest plots of the relationship on SNHG expression and prognosis after deleting "Lan T 2019": (A) OS, (B) DFS, (C) RFS.T A B L E 2 Subgroup analysis of SNHG expression for OS.

4 | 41 F I G U R E 4
DISCUSSIONLncRNAs are RNA molecules with a length more than 200 nucleotides that are incapable of being translated into proteins.Recent research has shown that lncRNAs regulate gene expression, which have a profound impact on various physiological and pathological processes.In particular, they can function as oncogenes or anti-oncogenes that directly or indirectly control signaling pathways associated with tumors and influence tumor progression.Begg's funnel plot of publication bias for OS, DFS, and RFS.Each circle represents an independent study.

. of studies No. of patients HR (95% CI) p Heterogeneity I 2 (%) p-value Model
Abbreviations: 95% CI, confidence interval; HR, hazard ratio.T A B L E 3 Meta-analysis results on the relationship between over-expressed SNHG and clinicopathological factors.